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Active object recognition for 2D and 3D applications
Includes bibliographical references
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2016
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| _version_ | 1867613294741684224 |
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| access_status_str | Open Access |
| author | Govender, Natasha |
| author2 | Nicolls, Frederick |
| author_browse | Govender, Natasha Nicolls, Frederick |
| author_facet | Nicolls, Frederick Govender, Natasha |
| author_sort | Govender, Natasha |
| collection | Thesis |
| description | Includes bibliographical references |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/16520 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:51.607Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/16520 Active object recognition for 2D and 3D applications Govender, Natasha Nicolls, Frederick Electrical Engineering object recognition systems Includes bibliographical references Active object recognition provides a mechanism for selecting informative viewpoints to complete recognition tasks as quickly and accurately as possible. One can manipulate the position of the camera or the object of interest to obtain more useful information. This approach can improve the computational efficiency of the recognition task by only processing viewpoints selected based on the amount of relevant information they contain. Active object recognition methods are based around how to select the next best viewpoint and the integration of the extracted information. Most active recognition methods do not use local interest points which have been shown to work well in other recognition tasks and are tested on images containing a single object with no occlusions or clutter. In this thesis we investigate using local interest points (SIFT) in probabilistic and non-probabilistic settings for active single and multiple object and viewpoint/pose recognition. Test images used contain objects that are occluded and occur in significant clutter. Visually similar objects are also included in our dataset. Initially we introduce a non-probabilistic 3D active object recognition system which consists of a mechanism for selecting the next best viewpoint and an integration strategy to provide feedback to the system. A novel approach to weighting the uniqueness of features extracted is presented, using a vocabulary tree data structure. This process is then used to determine the next best viewpoint by selecting the one with the highest number of unique features. A Bayesian framework uses the modified statistics from the vocabulary structure to update the system's confidence in the identity of the object. New test images are only captured when the belief hypothesis is below a predefined threshold. This vocabulary tree method is tested against randomly selecting the next viewpoint and a state-of-the-art active object recognition method by Kootstra et al.. Our approach outperforms both methods by correctly recognizing more objects with less computational expense. This vocabulary tree method is extended for use in a probabilistic setting to improve the object recognition accuracy. We introduce Bayesian approaches for object recognition and object and pose recognition. Three likelihood models are introduced which incorporate various parameters and levels of complexity. The occlusion model, which includes geometric information and variables that cater for the background distribution and occlusion, correctly recognizes all objects on our challenging database. This probabilistic approach is further extended for recognizing multiple objects and poses in a test images. We show through experiments that this model can recognize multiple objects which occur in close proximity to distractor objects. Our viewpoint selection strategy is also extended to the multiple object application and performs well when compared to randomly selecting the next viewpoint, the activation model and mutual information. We also study the impact of using active vision for shape recognition. Fourier descriptors are used as input to our shape recognition system with mutual information as the active vision component. We build multinomial and Gaussian distributions using this information, which correctly recognizes a sequence of objects. We demonstrate the effectiveness of active vision in object recognition systems. We show that even in different recognition applications using different low level inputs, incorporating active vision improves the overall accuracy and decreases the computational expense of object recognition systems. 2016-01-25T11:39:56Z 2016-01-25T11:39:56Z 2015 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/16520 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering object recognition systems Govender, Natasha Active object recognition for 2D and 3D applications |
| thesis_degree_str | Doctoral |
| title | Active object recognition for 2D and 3D applications |
| title_full | Active object recognition for 2D and 3D applications |
| title_fullStr | Active object recognition for 2D and 3D applications |
| title_full_unstemmed | Active object recognition for 2D and 3D applications |
| title_short | Active object recognition for 2D and 3D applications |
| title_sort | active object recognition for 2d and 3d applications |
| topic | Electrical Engineering object recognition systems |
| url | http://hdl.handle.net/11427/16520 |
| work_keys_str_mv | AT govendernatasha activeobjectrecognitionfor2dand3dapplications |